Answer:
a) No
b) 42%
c) 8%
d) X 0 1 2
P(X) 42% 50% 8%
e) 0.62
Step-by-step explanation:
a) No, the two games are not independent because the the probability you win the second game is dependent on the probability that you win or lose the second game.
b) P(lose first game) = 1 - P(win first game) = 1 - 0.4 = 0.6
P(lose second game) = 1 - P(win second game) = 1 - 0.3 = 0.7
P(lose both games) = P(lose first game) × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%
c) P(win first game) = 0.4
P(win second game) = 0.2
P(win both games) = P(win first game) × P(win second game) = 0.4 × 0.2 = 0.08 = 8%
d) X 0 1 2
P(X) 42% 50% 8%
P(X = 0) = P(lose both games) = P(lose first game) × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%
P(X = 1) = [ P(lose first game) × P(win second game)] + [ P(win first game) × P(lose second game)] = ( 0.6 × 0.3) + (0.4 × 0.8) = 0.18 + 0.32 = 0.5 = 50%
e) The expected value 
f) Variance 
Standard deviation 
Step One - List the factors of 60.
Step Two - Locate the factors that are seven apart from each other.
Factors of 60:
1 × 60
2 × 30
3 × 20
4 × 15
5 × 12
6 × 10
60 - 1 = 59
30 - 2 = 28
20 - 3 = 17
15 - 4 = 11
12 - 5 = 7
10 - 6 = 4
5 is 7 less than 12, and 60 is their least common multiple.
Answer: 5 and 12
Well, 4.2 divided by 8.19 = 0.51282051282 But if you switch it to 8.19 divided by 4.2 that would equal 1.95
Hope I helped!
- Debbie <span />
Answer:
B
I
Step-by-step explanation:
A triangle's angles must add up to 180
68+90+x=180
158+x=180
22=x
3 and 2 must add up to 180
70+x=180
angle 2= 110
angle 2 and angle 1 have to add up to 180 as well
110+x=180
andgle 1= 70
The difference between causation and correlation is that, Causation is characterized by cause-and-effect while correlation establishes a probable relationship.
<h3>What is the difference between Causation and correlation?</h3>
While Causation is characterized by a situation in which an action certainly causes an outcome and hence, is described as a cause-and-effect relationship, Correlation on the other hand only establishes a relationship between the two events and doesn't necessarily ascertain the occurrence of the other event .
An example of two variables which may be correlated is; the height and weight of an individual in which case it is generally perceived that taller people are heavier.
Read more on correlation and causation;
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